{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings #减少代码执行过程中的不必要提醒\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # type: ignore\n",
    "\n",
    "x = np.array([56, 72, 69, 88, 102, 86, 76, 79, 94, 74])\n",
    "y = np.array([92, 102, 86, 110, 130, 99, 96, 102, 105, 92])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt # type: ignore\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel(\"Area\")\n",
    "plt.ylabel(\"Price\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
